Bioinspired dynamic matrix based on developable structure of MXene-cellulose nanofibers (CNF) soft actuators
Inspired by natural organisms, soft actuators can convert environmental stimuli into mechanical deformation, making them indispensable for applications in a variety of fields such as soft robotics. MXene, displaying exceptional attributes in conductivity, thermal efficiency, good dispersibility etc....
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Main Authors: | , , , , , , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/174687 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Inspired by natural organisms, soft actuators can convert environmental stimuli into mechanical deformation, making them indispensable for applications in a variety of fields such as soft robotics. MXene, displaying exceptional attributes in conductivity, thermal efficiency, good dispersibility etc., has emerged as a preferred material for high-performance thermal actuators. However, single actuators struggle to achieve complex tasks realized by intelligent robotic systems. Herein, a bioinspired dynamic matrix is presented utilizing the developable structure of MXene-cellulose nanofibers (CNF) soft actuators. The inclusion of CNF considerably bolsters the mechanical properties of MXene. The MXene-CNF film possessed a higher working strain range (≈14%) than pure MXene film (≈2%). The designed developable structures complete the actuating movements. The sensing layer integration with the actuating layer led to an extremely low touch detection limit (0.3 kPa) and expedited actuating-feedback due to the interaction between contact charging and electrostatic induction. A responsive dynamic matrix containing 3 × 3 soft actuators, completed with a close-looped sensing-feedback function, is developed similar to the behavior of the mimosa plant. This mimosa-inspired dynamic matrix is capable of identifying the source of touch stimuli and providing immediate feedback. This research broadens the potential for enhancing adaptability and intelligence of soft robotic system. |
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